
CBO: Constrained Bayesian Optimization with learned reward and safety model [1]} We expect this baseline to be less data-efficient as the optimization happens directly on the policy parameter space.

SafeAPT (no GP-safety): An ablation baseline of our proposed algorithm, where sim-to-real safety transformation function is not learned from the real-world data. Instead, safety priors stored in the repertoire are assumed to be valid in the real world. This baseline shows the importance of learning the safety model from the real world interaction, even though the repertoire has policies that are potentially “at least safe” over a distribution of dynamics conditions.

SafeAPT (single dynamics): An ablation baseline of our proposed algorithm, where only one dynamics condition (randomly sampled from \(U(\Psi )\) ) is used to generate the policy repertoire. This baseline evaluates the importance of using multiple dynamics situations to generate the repertoire.

